function test_suite=test_target_dsm_corr_measure
% tests for cosmo_target_dsm_corr_measure
%
% # For CoSMoMVPA's copyright information and license terms, #
% # see the COPYING file distributed with CoSMoMVPA. #
try % assignment of 'localfunctions' is necessary in Matlab >= 2016
test_functions=localfunctions();
catch % no problem; early Matlab versions can use initTestSuite fine
end
initTestSuite;
function test_target_dsm_corr_measure_pearson
ds=cosmo_synthetic_dataset('ntargets',6,'nchunks',1);
mat1=[1 2 3 4 2 3 2 1 2 3 1 2 3 2 2];
dcm1=cosmo_target_dsm_corr_measure(ds,'target_dsm',mat1);
assertElementsAlmostEqual(dcm1.samples,0.2507,'absolute',1e-4);
assertEqual(dcm1.sa.labels,{'rho'});
assertEqual(dcm1.sa.metric,{'correlation'});
assertEqual(dcm1.sa.type,{'Pearson'});
distance_ds=cosmo_pdist(ds.samples,'correlation');
assertElementsAlmostEqual(cosmo_corr(distance_ds',mat1'),dcm1.samples);
sq1=cosmo_squareform(mat1);
dcm2=cosmo_target_dsm_corr_measure(ds,'target_dsm',sq1);
assertElementsAlmostEqual(dcm1.samples,dcm2.samples);
dcm2.samples=dcm1.samples;
assertEqual(dcm1,dcm2);
dcm3=cosmo_target_dsm_corr_measure(ds,'target_dsm',sq1,...
'metric','euclidean');
assertElementsAlmostEqual(dcm3.samples,0.3037,'absolute',1e-4);
function test_target_dsm_corr_measure_partial
ds=cosmo_synthetic_dataset('ntargets',6,'nchunks',1);
mat1=[1 2 3 4 2 3 2 1 2 3 1 2 3 2 2];
mat2=mat1(end:-1:1);
dcm1=cosmo_target_dsm_corr_measure(ds,'target_dsm',mat1,...
'regress_dsm',mat2);
assertElementsAlmostEqual(dcm1.samples,0.3082,'absolute',1e-4);
function test_target_dsm_corr_measure_partial_vector_partialcorr
if cosmo_skip_test_if_no_external('!partialcorr')
return;
end
ntargets=ceil(rand()*6+6);
ds=cosmo_synthetic_dataset('ntargets',ntargets,'nchunks',1);
ds.samples(:,:)=randn(size(ds.samples));
ncombi=ntargets*(ntargets-1)/2;
vec1=randn(1,ncombi);
vec2=randn(1,ncombi);
dcm1=cosmo_target_dsm_corr_measure(ds,'target_dsm',vec1,...
'regress_dsm',vec2);
distance=cosmo_pdist(ds.samples,'correlation');
pcorr=partialcorr(distance',vec1',vec2');
assertElementsAlmostEqual(dcm1.samples,pcorr);
mat1=cosmo_squareform(vec1);
mat2=cosmo_squareform(vec2);
dcm2=cosmo_target_dsm_corr_measure(ds,'target_dsm',mat1,...
'regress_dsm',mat2);
assertElementsAlmostEqual(dcm1.samples,dcm2.samples);
dcm1_s=cosmo_target_dsm_corr_measure(ds,'target_dsm',vec1,...
'regress_dsm',vec2,...
'type','Spearman');
pcorr_s=partialcorr(distance',vec1',vec2','type','Spearman');
assertElementsAlmostEqual(dcm1_s.samples,pcorr_s);
dcm2_s=cosmo_target_dsm_corr_measure(ds,'target_dsm',mat1,...
'regress_dsm',mat2,...
'type','Spearman');
assertElementsAlmostEqual(dcm1_s.samples,dcm2_s.samples);
function test_target_dsm_corr_measure_partial_cell_partialcorr
if cosmo_skip_test_if_no_external('!partialcorr')
return;
end
ntargets=ceil(rand()*6+6);
ds=cosmo_synthetic_dataset('ntargets',ntargets,'nchunks',1);
ds.samples(:,:)=randn(size(ds.samples));
ncombi=ntargets*(ntargets-1)/2;
vec1=randn(1,ncombi);
% set up regression elements
n_regress=ceil(rand()*5+3);
regress_vec_cell=cell(1,n_regress);
regress_mx=zeros(ncombi,n_regress);
for k=1:n_regress
v=randn(1,ncombi);
regress_vec_cell{k}=v;
regress_mx(:,k)=v;
end
dcm1=cosmo_target_dsm_corr_measure(ds,'target_dsm',vec1,...
'regress_dsm',regress_vec_cell);
distance=cosmo_pdist(ds.samples,'correlation');
pcorr=partialcorr(distance',vec1',regress_mx);
assertElementsAlmostEqual(dcm1.samples,pcorr);
mat1=cosmo_squareform(vec1);
regress_mx_cell=cell(size(regress_vec_cell));
for k=1:n_regress
regress_mx_cell{k}=squareform(regress_vec_cell{k});
end
dcm2=cosmo_target_dsm_corr_measure(ds,'target_dsm',mat1,...
'regress_dsm',regress_mx_cell);
assertElementsAlmostEqual(dcm1.samples,dcm2.samples);
function test_target_dsm_corr_measure_partial_regression
ds=cosmo_synthetic_dataset('ntargets',6,'nchunks',1);
vec1=[1 2 3 4 2 3 2 1 2 3 1 2 3 2 2];
vec2=vec1(end:-1:1);
dcm1=cosmo_target_dsm_corr_measure(ds,'target_dsm',vec1,...
'regress_dsm',vec2);
assertElementsAlmostEqual(dcm1.samples,0.3082,'absolute',1e-4);
mat1=cosmo_squareform(vec1);
mat2=cosmo_squareform(vec2);
dcm2=cosmo_target_dsm_corr_measure(ds,'target_dsm',mat1,...
'regress_dsm',mat2);
assertElementsAlmostEqual(dcm1.samples,dcm2.samples);
function test_target_dsm_corr_measure_partial_no_correlation
t=[0 1 0 0;
0 0 -1 0;
0 0 0 0;
0 0 0 0];
r=[0 0 1 0;
0 0 0 0;
0 0 0 0;
0 0 0 0];
t=t+t';
r=r+r';
msk=triu(ones(size(t)),1)>0;
c=cosmo_corr(t(msk),r(msk));
assertElementsAlmostEqual(c,0); % uncorrelated
ds=cosmo_synthetic_dataset('nchunks',1,'ntargets',4);
t_base=cosmo_target_dsm_corr_measure(ds,'target_dsm',t);
t_regress=cosmo_target_dsm_corr_measure(ds,'target_dsm',t,...
'regress_dsm',r);
assertElementsAlmostEqual(t_base.samples,-0.6615,'absolute',1e-4);
assertElementsAlmostEqual(t_regress.samples,-0.7410,'absolute',1e-4);
function test_target_dsm_corr_measure_non_pearson
% test non-Pearson correlations
ds=cosmo_synthetic_dataset('ntargets',6,'nchunks',1);
mat1=[1 2 3 4 2 3 2 1 2 3 1 2 3 2 2];
dcm1=cosmo_target_dsm_corr_measure(ds,'target_dsm',mat1,...
'type','Spearman');
assertElementsAlmostEqual(dcm1.samples,0.2558,'absolute',1e-4);
function test_target_dsm_corr_measure_glm_dsm
ds=cosmo_synthetic_dataset('ntargets',6,'nchunks',1);
ds_vec_row=cosmo_pdist(ds.samples,'correlation');
ds_vec=cosmo_normalize(ds_vec_row(:),'zscore');
mat1=[1 2 3 4 2 3 2 1 2 3 1 2 3 2 2];
mat2=mat1;
mat2(1:10)=mat2(10:-1:1);
nrows=numel(mat1);
design_matrix=[cosmo_normalize([mat1',mat2'],'zscore'),ones(nrows,1)];
betas=design_matrix \ ds_vec;
dcm1=cosmo_target_dsm_corr_measure(ds,'glm_dsm',{mat1,mat2});
assertElementsAlmostEqual(dcm1.samples,betas(1:2));
assertElementsAlmostEqual(dcm1.samples,[0.3505;0.3994],...
'absolute',1e-4);
sa=cosmo_structjoin('labels',{'beta1';'beta2'},...
'metric',{'correlation';'correlation'});
assertEqual(dcm1.sa,sa);
mat2=cosmo_squareform(mat2);
dcm2=cosmo_target_dsm_corr_measure(ds,'glm_dsm',{1+3*mat1,2*mat2});
assertElementsAlmostEqual(dcm1.samples,dcm2.samples);
assertEqual(dcm1.sa,dcm2.sa);
dcm3=cosmo_target_dsm_corr_measure(ds,'glm_dsm',{mat2,mat1});
assertElementsAlmostEqual(dcm1.samples([2 1],:), dcm3.samples);
assertEqual(dcm1.sa,dcm3.sa);
function test_target_dsm_corr_measure_glm_dsm_matlab_correspondence
if cosmo_skip_test_if_no_external('@stats')
return;
end
ds=cosmo_synthetic_dataset('ntargets',6,'nchunks',1);
ds.samples=randn(size(ds.samples));
ds_vec_row=cosmo_pdist(ds.samples,'correlation');
ds_vec=cosmo_normalize(ds_vec_row(:),'zscore');
mat1=rand(1,15);
mat2=rand(1,15);
design_matrix=cosmo_normalize([mat1',mat2'],'zscore');
beta=regress(ds_vec, design_matrix);
ds_beta=cosmo_target_dsm_corr_measure(ds,'glm_dsm',{mat1,mat2});
assertElementsAlmostEqual(beta, ds_beta.samples);
function test_target_dsm_random_data_with_cosmo_functions
ntargets=ceil(rand()*5+5);
nfeatures=ceil(rand()*20+30);
ds=struct();
ds.sa.targets=(1:ntargets)';
ds.sa.chunks=ceil(rand()*10+3);
% assume working pdist (tested elsewhere)
make_rand_dsm=@()cosmo_pdist(randn(ntargets,2*nfeatures));
target_dsm=make_rand_dsm();
glm_dsm={make_rand_dsm(),make_rand_dsm(),make_rand_dsm()};
for num_glms=0:numel(glm_dsm)
for center_data=[-1,0,1]
for use_mask=[-1,0,1]
ds.samples=randn(ntargets,nfeatures);
samples=ds.samples;
opt=struct();
% optionally, center data
if center_data>0
opt.center_data=logical(center_data);
if opt.center_data
samples=bsxfun(@minus,samples,mean(samples,1));
end
end
% compute pdist for samples
c=cosmo_squareform(1-cosmo_corr(samples'));
n_pairs=numel(c);
if use_mask>0
while true
msk=rand(n_pairs,1)>.5;
if sum(msk)>3
break;
end
end
else
msk=true(n_pairs,1);
end
if num_glms==0
opt.target_dsm=target_dsm;
opt.target_dsm(~msk)=NaN;
expected_samples=cosmo_corr(c(msk)',...
opt.target_dsm(msk)');
else
opt.glm_dsm=glm_dsm(1:num_glms);
for k=1:num_glms
opt.glm_dsm{k}(~msk)=NaN;
end
glm_mat=cat(1,opt.glm_dsm{:})';
glm_z=helper_quick_zscore(glm_mat(msk,:));
c_z=helper_quick_zscore(c(msk)');
expected_samples=glm_z \ c_z;
end
result=cosmo_target_dsm_corr_measure(ds,opt);
assertElementsAlmostEqual(result.samples,expected_samples)
end
end
end
function mat_z=helper_quick_zscore(mat)
mat_c=bsxfun(@minus,mat,mean(mat,1));
mat_z=bsxfun(@rdivide,mat_c,std(mat_c,[],1));
function test_target_dsm_corr_measure_mask_exceptions
ntargets=6;
ds=cosmo_synthetic_dataset('ntargets',ntargets,'nchunks',1);
npairs=ntargets*(ntargets-1)/2;
for num_non_nan=4:npairs
nan_msk=true(npairs,1);
rp=randperm(npairs);
nan_msk(rp(1:num_non_nan))=false;
for num_glms=-1:3
opt=struct();
if num_glms==-1
opt.regress_dsm=randn(npairs,1);
opt.regress_dsm(nan_msk)=NaN;
end
if num_glms<=0
opt.target_dsm=randn(npairs,1);
opt.target_dsm(nan_msk)=NaN;
else
opt.glm_dsm=cell(num_glms,1);
for k=1:num_glms
opt.glm_dsm{k}=randn(npairs,1);
opt.glm_dsm{k}(nan_msk)=NaN;
end
end
for set_inconsistent_non_nan_msk=[false,true]
key_cell=intersect(fieldnames(opt),...
{'glm_dsm','regress_dsm'});
if set_inconsistent_non_nan_msk && ...
(isempty(key_cell) || any(num_glms==[0,1]))
% skip
continue;
end
if set_inconsistent_non_nan_msk
assert(numel(key_cell)==1);
key=key_cell{1};
% swap true and false value in one of the matrices
value=opt.(key);
value_is_cell=iscell(value);
if value_is_cell
glm_idx=num_glms;
value=value{glm_idx};
end
i=find(isnan(value),1);
j=find(~isnan(value),1);
value(i)=value(j);
value(j)=NaN;
if value_is_cell
opt.(key){glm_idx}=value;
else
opt.(key)=value;
end
end
func_handle=@()cosmo_target_dsm_corr_measure(ds,opt);
expect_error=set_inconsistent_non_nan_msk;
if expect_error
assertExceptionThrown(func_handle,'');
else
% should be ok
func_handle();
end
end
end
end
% test exceptions
function test_target_dsm_corr_measure_exceptions
ds=cosmo_synthetic_dataset('ntargets',6,'nchunks',1);
mat1=[1 2 3 4 2 3 2 1 2 3 1 2 3 2 2];
aet=@(varargin)assertExceptionThrown(...
@()cosmo_target_dsm_corr_measure(varargin{:}),'');
aet(struct,mat1);
aet(ds);
aet(ds,'target_dsm',[mat1 1]);
aet(ds,'target_dsm',eye(6));
aet(ds,'target_dsm',zeros(7));
aet(ds,'target_dsm',mat1,'glm_dsm',{mat1});
aet(ds,'regress_dsm',mat1,'glm_dsm',{mat1});
aet(ds,'target_dsm',mat1,'glm_dsm',repmat({mat1},15,1));
aet(ds,'regress_dsm',mat1,'glm_dsm',repmat({mat1},15,1));
aet(ds,'glm_dsm',struct());
aet(ds,'glm_dsm',{[mat1 1]});
mat2_ds=struct();
mat2_ds.samples=mat1;
% requires numeric input
mat2_ds_rep=repmat({mat2_ds},1,15);
mat2_ds_stacked=cat(1,mat2_ds_rep{:});
aet(ds,'target_dsm',mat2_ds_stacked);
% illegal correlation type
aet(ds,'target_dsm',mat1,'type','foo');
aet(ds,'target_dsm',mat1,'type',2);
% Spearman or Kendall not allowed when using glm_dsm
aet(ds,'glm_dsm',mat1,'type','Spearman');
aet(ds,'glm_dsm',mat1,'type','Kendall');
% Kendall not allowed with regress_dsm
aet(ds,'target_dsm',mat1,'regress_dsm',{mat1},'type','Kendall');
function test_target_dsm_corr_measure_warnings_zero()
ds=cosmo_synthetic_dataset('ntargets',3,'nchunks',1);
ds_zero=ds;
ds_zero.samples(:)=0;
helper_target_dsm_corr_measure_with_warning(ds_zero,...
'target_dsm',[1 2 3]);
function test_target_dsm_corr_measure_warnings_nan()
ds=cosmo_synthetic_dataset('ntargets',3,'nchunks',1);
ds_zero=ds;
ds_zero.samples(1)=NaN;
helper_target_dsm_corr_measure_with_warning(ds_zero,...
'target_dsm',[1 2 3]);
function test_target_dsm_corr_measure_warnings_constant()
ds=cosmo_synthetic_dataset('ntargets',3,'nchunks',1);
ds_zero=ds;
helper_target_dsm_corr_measure_with_warning(ds_zero,...
'target_dsm',[0 0 0]);
function result=helper_target_dsm_corr_measure_with_warning(varargin)
% ensure to reset to original state when leaving this function
warning_state=cosmo_warning();
cleaner=onCleanup(@()cosmo_warning(warning_state));
% clear all warnings
empty_state=warning_state;
empty_state.shown_warnings={};
cosmo_warning(empty_state);
cosmo_warning('off');
result=cosmo_target_dsm_corr_measure(varargin{:});
w=cosmo_warning();
assert(numel(w.shown_warnings)>0)
assert(iscellstr(w.shown_warnings));
